107 Tasks Tested: Which Agent Framework Is More Reliable — LangGraph, CrewAI, or AutoGen?

Putting Agent Frameworks Through a Real-World Gauntlet

Most Agent framework marketing lacks actionable signal. The open-source project sweta2503/agent-framework-benchmark puts LangGraph, CrewAI, and AutoGen through the same 107 data engineering tasks side by side — no polished demos, just direct measurement under unified prompts, unified tools, and unified constraints.

These tasks cover multi-hop file extraction, conditional database writes, compositional retrieval and summarization, and more. These are exactly the scenarios where Agent orchestration is supposed to shine — but where errors compound quickly as abstractions start leaking.

The benchmark setup is straightforward: 107 representative data pipeline tasks, all using GPT-4o via the OpenAI API, evaluated on task pass rate (human-verified), latency, and token cost. What makes this methodology valuable is that it measures framework performance on real composite tasks, not single-point capabilities.

A multi-hop task is a composite task requiring multiple sequential reasoning or retrieval steps, where each step's output feeds into the next. For example, "extract fields from a PDF, then query a database using those field values, then write the results to S3" is a classic three-hop pipeline. The challenge for Agent frameworks is that intermediate state must be correctly propagated and serialized — a failure at any single hop can cause a silent failure rather than an explicit error. Compared to single-capability tests, multi-hop tasks are far better at exposing a framework's true weaknesses in state management, tool routing, and error recovery, which is why this benchmark uses them as its core scenario.

The Reliability-Cost Tradeoff: Every Framework Has Its Weakness

The raw results from direct log inspection are fairly clear:

Framework	Success Rate	Avg Completion Time (s)	Avg Token Cost
LangGraph	77.6%	31.2	12,143
CrewAI	63.6%	21.9	9,241
AutoGen	59.8%	29.4	13,031

LangGraph leads on reliability, especially for chained and multi-modal I/O tasks. Its graph-oriented execution model facilitates task delegation and debugging, but at the cost of higher token consumption and longer runtimes.

CrewAI aggressively trims tool calls and context, significantly reducing token usage. The tradeoff is more frequent tool handoff failures and agent stalls. CrewAI delivers clear benefits for linear workflows, but hits a reliability wall when it encounters dynamic logic.

AutoGen underperforms in production-grade benchmarks. Its flexible event model makes iterative group-chat collaboration easy to prototype, but stalls, silent retries, and unpredictable token consumption seriously undermine its practical utility.

LangGraph is based on a directed graph (DAG/cyclic graph) execution model that abstracts each Agent action as a node in the graph, with state flowing along edges — natively supporting branching, loops, and conditional transitions. This is the structural reason for its higher reliability on complex control-flow tasks. CrewAI's design philosophy is "role-driven" — it wraps multiple LLM instances as "crew members" with distinct responsibilities who collaborate through task delegation. Its token-trimming strategy essentially compresses the context window on each tool call to reduce cost. AutoGen uses an event-driven GroupChat model where multiple Agents autonomously negotiate task assignments through message passing. This grants enormous flexibility but makes execution paths difficult to predict statically, significantly reducing determinism in production environments.

Where Frameworks Break: Deadlocks and Silent Loops

The benchmark numbers are just the surface. The deeper hazards are the failures that only surface late in the process. These failure scenarios can be run directly against each framework to expose the real breaking points.

CrewAI: Silent Deadlocks in Nested Routing

When an IO mapping breaks in a tool delegation chain, CrewAI neither throws an error nor retries. The loop spins indefinitely with no surface-level logs. In an "extract-parse-upload" type task, if the output of parse_json isn't routed to upload_to_s3, the process hangs forever — no result, no warning, no log. For operations teams, this is the most dangerous type of failure.

AutoGen: Token Burn and Context Loss in Chat Coordination

AutoGen's problem is that minor tool format changes cause context to bleed away during event loop retries. A workflow that should be simple — read a CSV, extract names, write to a database — can trigger multiple GPT calls, consume over 5,000 tokens, and ultimately return an empty result [] with the group chat ending and no error thrown. Tokens burn, context evaporates through retries, and the run budget is exhausted with nothing to show for it.

The Overlooked Latency Truth: Framework Overhead Is the Real Culprit

Most documentation blames latency on model API calls. In real multi-Agent runs, that framing is misleading:

• LLM API call time: 45% of total latency (mean)
• Framework event loop, state/serialization: 36%
• Tool validation/transformation overhead: 19%

Across 107 tasks on CrewAI and AutoGen, more time was lost to framework overhead than to LLM inference. In other words, if you're hunting a latency problem, look at your orchestration code first — not the model or third-party API.

A sample from raw CrewAI logs is telling: in a task with a total runtime of 23.8 seconds, only 2.3 seconds could be attributed to model calls — 8.8 seconds went to framework mediation during Agent handoffs, and 8.6 seconds to context serialization and retries. The 10x inflation of "prompt execution" time had nothing to do with OpenAI infrastructure. It came from tool mediation and Agent bookkeeping overhead — something that's almost never labeled upfront.

Framework event loop and state serialization refers to the housekeeping work a framework performs before and after each tool call: serializing the current Agent state into a transmissible data structure, routing messages between multiple Agents, validating tool input/output formats, and rebuilding context on retries. These operations seem trivial in isolation, but they accumulate linearly in composite tasks involving multiple tool calls. Take Python's pickle serialization or JSON conversion as examples — repeated serialization and deserialization of large context objects can easily add up to several seconds of latency. This is why framework overhead (36%) nearly rivals model API calls (45%) in real multi-Agent runs, rather than being negligible background noise.

Hand-Written Agents: Trading "Autopilot" for Transparency

Hand-written approaches like sleepworm/agent-from-zero and Adamsautomations/crewai-docs-copilot strip away the abstraction layers. What you get is direct task dispatch, clear error exposure, and explicit token accounting.

The same "extract-read-upload" workflow in a hand-written version behaves in a fully controllable way: errors surface as exceptions rather than hiding inside obscure loops; token usage per call is explicitly visible via res['usage']; and every control path and failure is auditable.

Hand-written Agents excel at debugging, testability, and operational support. You rebuild the orchestration logic and templates yourself, trading abstraction for complete visibility and control. It's a tradeoff many teams haven't fully priced in before their first real production deployment.

A hand-written Agent is essentially a return to directly calling the OpenAI (or other LLM) API and managing the tool-call loop yourself — typically implemented as an explicit while loop: check whether the model response contains tool_calls, execute the corresponding functions if so, append results to the message list, and repeat until the model returns a final text response. In this pattern, every input/output, token consumption, and error path is fully under the developer's control, with no third-party abstraction layer inserting hidden retries or context truncation logic. The cost is that developers must implement from scratch what frameworks provide out of the box: inter-Agent task dispatch, prompt template management, concurrency control, and more. For production systems with relatively fixed task logic and extremely high observability requirements, this engineering investment is often worth it.

Decision Guide: Use a Framework or Roll Your Own?

A side-by-side comparison based on real run data:

Metric	LangGraph	CrewAI	AutoGen	Hand-Written
Success Rate %	77.6	63.6	59.8	100*
Token Cost	12,143	9,241	13,031	8,900–12,500
Latency (s)	31.2	21.9	29.4	14–36
Debuggability	High	Low	Low	Explicit
Silent Failures	Rare	Common	Common	Never

*Note: The "100%" success rate for hand-written solutions is true by definition — code written for a specific scenario passes as long as the engineering is sound. There are no hidden orchestration states.

Framework selection guidance:

• LangGraph is best for multi-hop pipelines and when debugging control flow matters more than cost. It handles complex data flows with high branching logic well, but is not suited for high-frequency/low-latency operations.
• CrewAI is best when token cost is the top priority and workflows are linear and predictable. Accept more silent failures; operational burden increases as complexity grows.
• AutoGen is best for group-chat collaboration prototypes and experimental coordination. Suitable for R&D, but not production environments without extensive guardrails and tracing.
• Hand-written Agents are best when you need complete observability and must diagnose edge failures immediately. The cost is implementing orchestration, error routing, and logging from scratch.

The core takeaway is simple: choose a framework based on debugging clarity and production support capability — not how smooth the demo looks. Abstraction layers always leak under real load. Before trading transparency for convenience, run your own benchmark first.